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Analysis of flight delays in aviation system using different classification algorithms and feature selection methods

Published online by Cambridge University Press:  05 August 2019

A. B. A. Anderson ,
A. J. Sanjeev Kumar  and
A. B. Arockia Christopher Open the ORCID record for A. B. Arockia Christopher [Opens in a new window]
A. B. A. Anderson
Affiliation:
Department of CSE Veerammal Engineering CollegeDindigul, TN, India
A. J. Sanjeev Kumar
Affiliation:
Department of MCA Thiagarajar College of Engineering, Madurai, TN, India
A. B. Arockia Christopher*
Affiliation:
Department of IT, Dr. Mahalingam College of Engineering & TechnologyPollachi, TN, India
*
[email protected]
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Abstract

Data mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.

Keywords

Data miningFlight delayClassification techniquesFeature selectionAviation
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1267 , September 2019 , pp. 1415 - 1436
Copyright
© Royal Aeronautical Society 2019 

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References

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